Estimated US Pediatric Hospitalizations and School Absenteeism Associated With Accelerated COVID-19 Bivalent Booster Vaccination

Key Points Question Would accelerated COVID-19 bivalent booster vaccination uptake in the US be associated with decreased outcomes of pediatric hospitalizations and student absenteeism? Findings In this decision analytical model using US population estimates, a simulation model revealed that booster campaigns achieving an uptake similar to seasonal influenza vaccination could have prevented an estimated 10 019 pediatric hospitalizations and 5 448 694 days of school absenteeism from October 1, 2022, to March 31, 2023. Meaning These findings suggest that although COVID-19 prevention strategies often focus on older populations, the benefits of booster campaigns for children may be substantial.


SARS-CoV-2 variants
For the calibration and fitting the model to incidence data, we considered the spread of five variants, including Iota (B.1.526), Alpha (B.1.1.7), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529), in addition to the original Wuhan-Hu-I SARS-CoV-2 strain. All variants were introduced in the model at a date corresponding to twice the average duration of their estimated incubation period before the date of identification reported in the GISAID database. 7 Specifically, the Iota variant was introduced on October 25, 2020, with an estimated 35% higher transmissibility compared with the original Wuhan-Hu-I strain. 8 We then introduced the Alpha variant on November 29, 2020, with a 50% higher transmissibility compared to the original strain. 9

Distribution of disease stages and infectiousness
The incubation period for each individual infected with a previous variant (Original, Iota, and Alpha) was sampled from a log-normal distribution with a mean of 5.2 days. 14 A proportion of infected individuals progressed to a pre-symptomatic stage 15,16 with an infectious period which was sampled from a Gamma distribution with a mean of 2.3 days. 15,17 The symptomatic disease following the pre-symptomatic stage had an average infectious period of 3.2 days, which was also sampled from a Gamma distribution. 15,17 The infectious period of individuals who remained asymptomatic was sampled from a Gamma distribution with a mean of 5 days. 15,18 The incubation period for the Delta and Omicron variants were shorter [19][20][21][22] . For Delta, the incubation period was sampled from a lognormal distribution with a mean of 4.3 days, and a pre-symptomatic duration of 2 days on average [20][21][22] . The incubation period for Omicron was also sampled from a log-normal distribution, but with a mean of 3.3 days 21 . The mean duration of the presymptomatic stage for Omicron was assumed to be the same as Delta with an average of 2 days.
Infectiousness was assumed to be highest during the pre-symptomatic stage. The transmissibilities during asymptomatic, mild symptomatic, and severe symptomatic stages were 26%, 44%, and 89%, respectively, relative to the pre-symptomatic stage. 17

Disease outcomes
We assumed that asymptomatic and mild symptomatic individuals recover without hospitalization. Self-isolation was implemented to start within 24 hours of symptom onset for all symptomatic individuals, reducing their number of daily contacts by an average of 74% (eTable 1). Severely ill individuals due to primary infection were hospitalized within 2-5 days of symptom onset, 25,26 and therefore effectively excluded from the chain of disease transmission. The model was parameterized with rates of intensive care unit (ICU) and non-ICU admissions. [27][28][29] The risk of hospitalization with the Delta variant was assumed to be 2.26 times higher than that due to infection with Alpha. 29 We considered a 75.2% (95% confidence interval: 72.0% -77.0%) risk reduction of hospitalization for severe disease due to infection by Omicron compared to Delta. 30,31 The risk of ICU admissions was reduced by 38.1% in severe patients of Omicron compared to those infected with Delta. 32

Vaccination and immune dynamics
The number of vaccine doses per day and distribution of the first, second, and booster doses were parameterized with reported vaccination data in different age groups. 33 Following the start of vaccination, the booster eligibility was set to a 6-month period elapsed since the last dose of vaccine in fully vaccinated individuals. On January 3, 2022, this timeline was reduced to 5 months. 34 Given new guidelines, we simulated the model with a 4-month lag between the last vaccine dose or previous infection and a bivalent booster for eligible individuals.
We performed a literature review to derive the effectiveness estimates following each dose of the monovalent vaccines against infection, symptomatic disease, and severe disease for all variants in the model. Estimates of monovalent vaccine effectiveness for different SARS-CoV-2 variants in the model are summarized in eTables 3 and 4. We assume that the effectiveness of a bivalent booster against infection, symptomatic infection, and severe disease caused by more recent Omicron variants (BA.4/5, BQ.1, BQ.1.1) would be the same as the corresponding protection of a monovalent booster dose against the Omicron BA.1 variant.
To implement waning immunity after vaccination, we fitted a Gaussian model to estimates of vaccine effectiveness over time, [45][46][47][48] and determined the temporal relative effectiveness curves (eFigure 1). The relative effectiveness was then used as a multiplicative factor on the effectiveness of vaccines after the second or booster dose to determine the temporal immunity of individuals against infection and severe disease for each variant. We applied the same relative effectiveness for waning of naturally-acquired immunity. However, natural immunity was associated with 3.1 times (95% confidence interval: 1.4 -4.8) lower risk of hospitalization compared to fully vaccinated individuals without a booster and no prior infection when Delta was the predominant variant. 49 We considered primary, booster, and hybrid immunity in the model. Although hybrid immunity from infection plus two to three vaccine doses is shown to increase vaccine effectiveness with longer durability, 50,51 we did not have any specific quantification of such effectiveness for the duration of the study. We therefore conservatively assumed that vaccination of those with a previous infection will lead to the same protection estimated for the effectiveness of the second dose in the primary series or booster doses.

Model implementation
With the transmission probability derived from the calibration process, we fitted the model to incidence per 100,000 population from October 1, 2020 to September 30, 2022. 52 We chose October 1 as the starting point for our calibration and simulations because it was a time of a relatively low incidence preceding the fall/winter wave in the US, the launch of the vaccination campaign, and emergence of different variants. The pre-existing immunity against COVID-19 was included in the model using a probability distribution function, based on the reported incidence from the beginning of pandemic to the end of September 2020 to account for waning immunity over time (eFigure 1). At the start of fitting (October 2020), we assumed that contacts between individuals did not exceed 50% of the pre-pandemic level. 53 During the calibration process (in the presence of only the original strain of SARS-CoV-2), we determined the transmission probability of 0.0345 that minimized the difference between the mean of cumulative incidence predicted by running independent realizations in the model and cumulative reported cases over time. The transmission probability obtained during the calibration corresponds to an effective reproduction number of 1.17 in early October 2020. 54 After the calibration, the transmission probability of the original strain remained fixed, and the age-specific contact rates were adjusted to minimize the difference between the temporal cumulative incidence predicted by the model and the cumulative reported cases, implicitly accounting for the change and effect of various non-pharmaceutical measures. During fitting, the transmissibility of other variants was adjusted relative to the preceding variant(s) at the time they were introduced in the model. We then ran 500 independent Monte-Carlo simulations for the study period, and determined the 95% credible intervals using a bias-corrected and accelerated bootstrap method (with 500 replications), which corrects for bias and skewness in the distribution of bootstrap estimates when scaled from the per capita to the entire US population. The model was implemented in Julia, and simulation codes are available at: https://github.com/thomasvilches/USomicron/tree/booster_scenarios

Additional results
We simulated the model when only 50% of mildly symptomatic cases follow guidelines for isolation. eFigure 2 illustrates the model projection for the incidence per 100,000 population with simulated scenarios of bivalent booster vaccination from October 1, 2022 to end of March 2023. Averted outcomes for isolation days, hospitalizations, and ICU hospitalizations were estimated for the entire pediatric population aged 0-17 years. Averted days of school absenteeism were estimated for children aged 5-17 years. Accelerated bivalent booster coverage among eligible individuals were: (Scenario 1) equal to half of the 2020-21 age-specific influenza vaccination levels; and (Scenario 2) equal to the 2020-21 age-specific influenza vaccination levels.